使用新的非线性自回归分布式滞后模型预测天气集成人类血清病预测系统的不对称效应
Yongbin Wang1, Chenlu Xue1, Bingjie Zhang1
1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang 453003, Henan Province, China.
Transboundary and emerging diseases
|April 30, 2025
概括
在中国,受天气影响的人类百菌病 (HB) 发病率正在上升. 这项研究发现风速和湿度对HB的不对称影响,非线性模型准确地预测了疫情爆发.
科学领域:
- 流行病学 流行病学
- 环境健康 环境健康
- 生物统计学 生物统计学
背景情况:
- 人类血病 (HB) 在中国构成了重大公共卫生挑战,其发病率在2005-2020年间呈现上升趋势.
- 了解气象变量对HB的影响对于开发有效的预警系统和公共卫生干预至关重要.
研究的目的:
- 研究气象变量的短期和长期不对称影响,对中国人类血病发病率的研究.
- 开发和验证使用非线性自回归分布式滞后 (NARDL) 模型的HB早期预测系统.
主要方法:
- 从2005年到2020年,利用了关于HB发病率和气象变量 (平均风速,平均相对湿度,平均温度,平均气压,总降水量,总日照时间) 的月度数据.
- 使用自回归分布式滞后 (ARDL) 和非线性ARDL (NARDL) 模型来分析不对称的长期和短期影响.
- 将数据分为培训 (2005-2019) 和测试 (2020) 集,用于模型验证和预测准确性评估.
主要成果:
- 平均风速 (AWV) 的1米/秒变化和平均相对湿度的1小时变化显著影响了HB发病率.
- 平均温度和平均气压显示出对HB的长期线性影响.
- 与ARDL模型相比,NARDL模型在预测HB发病率方面显示出明显较低的错误率,这凸显了整合气候变量的重要性.
结论:
- 气象变量对人类血病发病率有显著的长期和短期不对称的影响.
- 通过纳入气候因素,NARDL模型有效地捕捉了HB流行病的动态模式.
- 建议将气象数据整合到公共卫生战略中,以加强中国的HB预防和控制.
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